PDFTron Systems Inc. specializes in PDF, SVG, XPS and other graphics technologies. As a leading global provider of cross-platform, high-performance products, it has helped customers worldwide from different disciplines. Based on the success of its product – PDFNet SDK on desktops, PDFTron is motivated to expand their technology to a fast-growing market – hand-held touch screen devices. But with limited CPU and display surfaces of tables, new challenges emerge to minimize lag, flicker, and other viewing artifacts. PDFTron has developed a tiling-algorithm to address some of these issues.
Using a multiphysics approach, the intern will construct a model for the topological optimization of two distinct classes of cantilever-type sensors: one often encountered in the general industrial application and the other one customized for the specific needs of a biosensor. Altair Hyperworks will be the main software tool to be used for this particular type of optimization and the aforementioned type of sensors will be modeled and their behavior simulated in complex multiphysics conditions.
Janro Imaging Laboratory (JIL) will work with researchers at Concordia and Emily Carr universities to develop 3D motion-tracking based interface tools for drawing and content creation in spatial drawing applications. As a basis for applied design research, researchers will use an alpha version of the “Optical Wand,” a product currently in development at JIL for use with SANDDE, the animator works in the stereoscopic environment itself and can therefore make changes intuitively and rapidly to the spatial composition of their work in 3D and in real time.
In this internship we aim to develop an analytics system that targets quick conversion of game data to knowledge that allows game designers to quickly grasp the sources of engagement and disengagement of users while interacting with video games. This proposed system can benefit Blackbird Interactive Inc. by providing a method to tune their designs based on a deeper understanding of their players.
This research aims to develop a system that generates explainable recommendations. Mobio currently allows merchants to offer items to users, but does not employ recent advances in recommender systems. This project will allow Mobio to build on the expertise of Dr. Chiang in related relational learning problems to create such a system, and provide a real-world domain for him to advance the state-of-the-art. A core problem in recommender systems is building models of user preference that are predictive and explainable. We propose to apply and evaluate algorithms developed by Dr.